The Financial Mechanics of Compute Debt In corporate accounting, cloud infrastructure assets and enterprise datacenters have traditionally been depreciated over a five-to-seven-year straight-line schedule. Servers running enterprise resource planning (ERP) databases or web servers from 2018 remain fully functional for basic enterprise compute tasks today. However, AI accelerators follow an aggressive silicon cadence where architectural generation transitions (e.g., Hopper H100 to Blackwell B200 to Rubin R100) deliver 2.5x to 4.0x increases in FP4/FP8 compute density and memory bandwidth every 18 to 24 months. Applying a standard 5-year depreciation model to AI clusters creates massive phantom profits on corporate income statements and threatens catastrophic balance sheet write-downs. The Real Economic Depreciation Curve In competitive commercial inference markets, token cost is directly tied to hardware efficiency: Cost per Million Tokens = (Hourly GPU Rental + Electricity + Facility Amortization) / Output TPS When a new chip architecture doubles output throughput while maintaining identical rack power consumption, the economic clearing price per token drops by half. Older generation silicon cannot compete at cost parity, forcing immediate price discounting and accelerating secondary market residual value degradation. Cost of Capital in High-Interest Rate Regimes During the zero-interest rate policy (ZIRP) era of 2010 to 2021, multi-year infrastructure investments were financed with near-zero capital costs. In an environment with 4.5% to 5.5% baseline policy rates, financing a $500 million GPU cluster using debt or equipment lease financing requires debt service payments that consume a substantial fraction of gross operating cash flow. Infrastructure operators who fail to model a conservative 36-month accelerated depreciation schedule risk severe technical insolvency when subsequent silicon nodes render their debt-encumbered clusters economically uncompetitive.